Predictive Analytics for Inpatient Postoperative Opioid Use in Patients Undergoing Mastectomy.

Autor:	Dolendo IM; Anesthesiology, University of California San Diego, La Jolla, USA., Wallace AM; Surgery, University of California San Diego, La Jolla, USA., Armani A; Surgery, University of California San Diego, La Jolla, USA., Waterman RS; Anesthesiology, University of California San Diego, San Diego, USA., Said ET; Anesthesiology, University of California San Diego, La Jolla, USA., Gabriel RA; Anesthesiology, University of California San Diego, La Jolla, USA.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2022 Mar 11; Vol. 14 (3), pp. e23079. Date of Electronic Publication: 2022 Mar 11 (Print Publication: 2022).
DOI:	10.7759/cureus.23079
Abstrakt:	Introduction: The use of opioids in mastectomy patients is a particular challenge, having to balance the management of acute pain while minimizing risks of continuous opioid use postoperatively. Despite attempts to decrease postmastectomy opioid use, including regional anesthetics, gabapentinoids, topical anesthetics, and nonopioid anesthesia, prolonged opioid use remains clinically significant among these patients. The goal of this study is to identify risk factors and develop machine-learning-based models to predict patients who are at higher risk for postoperative opioid use after mastectomy. Methods: In this retrospective cohort study, we collected data from patients that underwent mastectomy procedures. The primary outcome of interest was defined as oxycodone milligram equivalents (OME) greater than or equal to the 75% of OME use on a postoperative day 1. Model performance (area under the receiver-operating characteristics curve (AUC)) of various machine learning approaches was calculated via 10-fold cross-validation. Odds ratio (OR) and 95% confidence intervals (CI) were reported. Results: There were a total of 148 patients that underwent mastectomy and were included. The medium (quartiles) postoperative day 1 opioid use was 5 mg OME (0.25 mg OME). Using multivariable logistic regression, the most protective factors against higher opioid use was being postmenopausal (OR: 0.13, 95% CI: 0.03-0.61, p = 0.009) and cancer diagnosis (OR: 0.19, 95% CI: 0.05-0.73, p = 0.01). The AUC was 0.725 (95% CI: 0.572-0.876). There was no difference in the performance of other machine-learning-based approaches. Conclusions: The ability to predict patients' postoperative pain could have a significant impact on preoperative counseling and patient satisfaction. Competing Interests: Rodney Gabriel serves on the advisory board for Avanos and Acacia Pharmaceuticals. Rodney Gabriel and Engy Said’s institution has received research funding from Epimed, Teleflex, and SPR Therapeutics. (Copyright © 2022, Dolendo et al.)
Databáze:	MEDLINE
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